جست‌وجوی استاد و پژوهشگر

• Supply Chain Management
• Business Analytics
• Operations Research
• +۳ مورد دیگر

João Nuno Gonçalves is a full-time Invited Assistant Professor in Data Science at Universidade Católica Portuguesa (UCP) and a Researcher at CEGE - Research Centre in Management and Economics, Católica Porto Business School. He also holds academic positions at the University of Porto and University of Minho. He earned his PhD in Industrial and Systems Engineering (Business Analytics) from the University of Minho in 2021, following a Master's (1st in class) and Bachelor's in Mathematics (Top 10%). His research focuses on business analytics models for improving supply chain management (SCM), including demand forecasting and sequential decision-making. His work has been recognized with awards such as the Logistics Excellence Award Academy 2022 (APLOG) and multiple pedagogical merit distinctions. He coordinates the BSc in Applied Data Science and serves on the board of the Institute of Computing and Data Science at UCP. Research interests span decision support systems, supply chain optimization, and operations research. He actively contributes to academic success through predictive modeling and has supervised research in warehouse operations and fraud management. He participates in projects like UCP4SUCCESS (2024-2026) and reviews for journals like Computers and Operations Research and Journal of Purchasing and Supply Management . Education: PhD in Industrial and Systems Engineering (2017–2021), University of Minho Master's in Systems Engineering (2015–2017), University of Minho Bachelor's in Mathematics (2012–2015), University of Minho His pedagogical excellence is highlighted by six awards for teaching in Operations Research and Supply Chain Management. Research collaborations extend to logistics, automotive industries, and cybersecurity, emphasizing practical implementation of analytics.

John Oyekanمشاهده پروفایل

مدرس ارشد

• Human-Robot Collaboration
• Swarm Robotics
• Human-Centred AI
• +۱۲ مورد دیگر

Dr. John Oyekan is a Senior Lecturer in Human-Centred AI for Autonomous Manufacturing at the Department of Computer Science, University of York. He leads the Flexible Automation Lab within the £15 million Institute for Safe Autonomy, focusing on applied and fundamental research in robotics, AI, and digital manufacturing. His work bridges academia and industry, collaborating with partners in automotive, aerospace, and manufacturing sectors. Education: Ph.D. in Computer Science and Electronic Engineering, University of Essex MSc in Robotics and Embedded Systems, University of Essex His research spans three major themes: safe human-robot collaboration , enabling cage-free co-working environments; digital twins for zero-prototyping of factory layouts using VR and simulation; and nature-inspired algorithms for robotic exploration in unknown environments, inspired by animal foraging behavior. These interests converge on intelligent, adaptive manufacturing systems that enhance efficiency and safety. While specific publications are not listed, his work is characterized by over 40 publications in swarm robotics, manufacturing informatics, bio-inspired algorithms, and sensing, reflecting a strong trajectory in applied AI and robotics for industrial applications. Scientific Recognition: Chartered Engineer (CEng) Dr. Oyekan actively supervises PhD students and seeks new candidates, with funded positions available for home/EU students. His research is supported by collaborations with the Advanced Manufacturing Research Centre (Sheffield), Institute of High Performance Computing (Singapore), and government-funded catapults. He emphasizes translating blue-sky research into practical industrial solutions. He leads the Flexible Automation Lab , which develops cognitive algorithms for human-centred flexible automation across industrial sectors, working closely with industry partners and focusing on real-world impact aligned with Industry 5.0 principles.

Liucheng Guoمشاهده پروفایل

پژوهشگر ارشد

• AI software-hardware co-design
• embedded AI
• high-performance computing (HPC)
• +۷ مورد دیگر

Dr. Liucheng Guo is a Visiting Senior Research Fellow and RAEng Industrial Fellow at King’s College London, affiliated with the Department of Mathematics within the Faculty of Natural, Mathematical & Engineering Sciences. His expertise spans AI software-hardware co-design, embedded AI, and high-performance computing (HPC), with a focus on sustainable human-machine interfaces (HMI) for sectors like IoT, Automotive, Healthcare, and XR. He holds a Ph.D. from Imperial College London and an MA from Peking University. Dr. Guo has co-founded the award-winning AI startup TG0, securing seven patent groups and over £20 million in grants from InnovateUK, RAEng, and the European Innovation Council. His research emphasizes smart home, IoT, and healthcare applications, including VR mirror therapy for post-stroke rehabilitation and gesture-based control systems. He is a Senior Member of IEEE and Fellow of IET/BCS/RSA, reflecting his leadership in bridging research and industry. His grants include Smart Grants, KTP awards, and SME Leader initiatives. He actively promotes STEM through roles as RAEng Visiting Professor and public engagement ambassador.

• Electrical Machines and Energy Conversion
• Power Electronics
• Permanent Magnet Synchronous Motors
• +۳ مورد دیگر

Mikail KOÇ is an Associate Professor at the Faculty of Engineering and Architecture , Kırşehir Ahi Evran University , specializing in Electrical Machines and Energy Conversion and Power Electronics . His research focuses on advanced control strategies for Interior Permanent Magnet Synchronous Motors (IPMSM) and their applications in electric vehicles. BSc: Eskişehir Osmangazi University, Department of Electrical and Electronics Engineering (2009) Current Position: Associate Professor (since 2024), Full-Time His work emphasizes Maximum Torque per Ampere (MTPA) control methods, inverter nonlinearity compensation , and efficiency optimization in electric drives. He has developed innovative approaches using virtual signal injection, model predictive control, and hybrid flux observers for IPMSM systems. Research trends from his publications (19 articles in SCI/ESCI journals and conferences) include: Advanced control algorithms for electric vehicles Torque ripple and current distortion mitigation Adaptive parameter estimation techniques Optimization of switching frequencies and PWM strategies Integration of machine learning with motor control Mikail collaborates with international researchers (e.g., Sun, Wang) and has presented at IEEE conferences. His work contributes to energy-efficient and high-performance motor drives, addressing critical challenges in electrified transportation systems.

Peter Chienمشاهده پروفایل

استاد

• Verification, Validation and Uncertainty Quantification
• Decision-Making under Uncertainty
• Statistical Machine Learning
• +۲ مورد دیگر

Peter Chien is a Professor in the Department of Statistics at the University of Wisconsin–Madison, where he leads the Mendota Research Group focused on developing statistical and AI methodologies for real-world applications across engineering and industry. His core research encompasses: Verification, Validation and Uncertainty Quantification Decision-Making under Uncertainty Statistical Machine Learning and AI Design of Experiments for Modern Applications A/B Testing in online environments His work demonstrates significant industrial impact with adoption by Fortune 500 companies spanning aerospace, automotive, pharmaceuticals, semiconductors, and medical devices. Prof. Chien actively seeks new students and collaborators; contact details are available at peter.chien@wisc.edu .

• Autonomous Vehicles
• Software Engineering
• Machine Learning
• +۳ مورد دیگر

Pavle Dakic is affiliated with Singidunum University as a researcher in the Faculty of Informatics and Computing . His work spans multiple disciplines including Software Engineering , Machine Learning , and Autonomous Vehicles . Doctoral studies in Electrical Engineering and Computing at Singidunum University (2015-2024) Doctoral studies in Applied Informatics at Slovak Technical University (2020-2024) Master’s in Contemporary Information Technologies at Singidunum University (2014-2015) His research focuses on CI/CD pipelines , automotive software compliance , and cybersecurity in autonomous systems. He has published extensively on topics like intrusion detection in IoT/IIoT and AI-powered call centers . Recent publications include work on AI integration in business , robotic vehicle sustainability , and metaheuristic optimization for intrusion detection . His collaborations span international institutions and researchers.

Mai Abdelhakimمشاهده پروفایل

دانشیار

• Cybersecurity
• Cyber-Physical Systems
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Mai Abdelhakim is an Associate Professor at the University of Pittsburgh's Swanson School of Engineering, specializing in Electrical and Computer Engineering. Her research focuses on cybersecurity, cyber-physical systems, and artificial intelligence, particularly in designing secure and reliable IoT-enabled infrastructure. PhD in Electrical Engineering, Michigan State University MS and BS in Electronics and Communications Engineering, Cairo University Her work leverages stochastic analysis, information theory, and machine learning to address challenges in wireless communications and network security. She has published extensively on topics like multihop IoT networks, anomaly detection in autonomous vehicles, and robust image watermarking. Dr. Abdelhakim has received the University of Pittsburgh’s Innovation in Education award (2023) and serves as a Senior Editor for IET Communications since 2023. Her collaborations span cybersecurity, wireless systems, and machine learning applications in network reliability.

Florian De Vuystمشاهده پروفایل

استاد

• Fluid-Structure Interaction
• Scientific Machine Learning
• Reduced-Order Modeling
• +۶ مورد دیگر

Florian De Vuyst is a Full Professor at the Department of Computational Engineering, University of Technology of Compiègne (UTC), where he conducts research at the Biomechanical Bioengineering Laboratory (BMBI UMR 7338). Previously affiliated with the Applied Mathematics Laboratory (LMAC), his work spans Fluid-Structure Interaction (FSI) , Scientific Machine Learning , GPU Computing , and Reduced-Order Modeling (ROM) . His research focuses on multimaterial flows , automotive crash dynamics , and bioengineering applications like microcapsule deformation in Stokes flows. His recent publications highlight machine learning integration with FSI simulations and GPU-accelerated solvers for compressible flows. Collaborations include Renault , Michelin , and ENS Paris-Saclay on projects like tsunami coastal impact modeling (DIGISCOPE EquipEx) and vehicle drag estimation . He has supervised 15+ PhD students, including Azzedine Tiba (non-intrusive ROM) and Vincent Mahy (multimaterial methods). Scientific Awards: Recipient of the Best Applied Paper Award at EGC 2008 Grants & Collaborations: Involved in CNRS Editions' interdisciplinary volume on tsunamis, ERCOFTAC symposia, and industrial partnerships

• Advanced Manufacturing Processes
• Metal Forming Technologies
• Biomedical Implant Manufacturing
• +۵ مورد دیگر

Antonio Piccininni serves as an Assistant Professor in the Department of Mechanics, Mathematics & Management at Polytechnic University of Bari, Italy. His academic focus centers on advanced manufacturing technologies with particular expertise in metal forming processes, biomedical implant production, and sustainable manufacturing techniques. His research interests span multiple domains of manufacturing engineering, with emphasis on incremental forming , hydroforming , laser-assisted manufacturing , and superplastic forming techniques. Dr. Piccininni has made significant contributions to the development of customized biomedical implants, particularly cranial prostheses using titanium alloys, and has pioneered research in eco-friendly manufacturing processes including sustainable lubricants for metalworking applications. Analysis of his publication record reveals a clear progression from fundamental metal forming research toward increasingly specialized applications in biomedical engineering and sustainable manufacturing. His recent work demonstrates sophisticated integration of computational methods including machine learning, finite element analysis, and multi-criteria optimization techniques to solve complex manufacturing challenges. The trend shows growing emphasis on biomedical applications and environmental sustainability in manufacturing processes. Dr. Piccininni's research has practical applications in multiple industries including medical device manufacturing, automotive components production, and sustainable metalworking practices. His work bridges theoretical modeling with experimental validation, often incorporating advanced materials like titanium alloys and aluminum for specialized applications requiring high precision and biocompatibility.

Ulaş YURTSEVERمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Ulaş YURTSEVER serves as Assistant Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences. He has held academic positions since 1999, including Research Assistant roles at the Computer Research and Application Center (2005-2013) and Faculty of Engineering (1999-2013), and has been Lecturer at Adapazari Vocational School since 2013. His academic credentials include: Doctorate in Computer and Information Engineering (2007-2019): Thesis on "Colon cancer detection using deep learning method on segmented histopathological images" Master's in Electrical and Electronics Engineering (2000-2002): Thesis on "PC-based building automation" Bachelor's in Electrical and Electronics Engineering (1994-1999) Dr. YURTSEVER's research integrates Artificial Intelligence, Machine Learning, and Deep Learning to solve critical problems in medical diagnostics and environmental sustainability. His work focuses on computer vision applications for colon cancer detection in histopathological images and machine learning-driven analysis of plastic degradation across environmental matrices. He actively develops IoT systems for predictive maintenance in industrial settings. His publication trajectory (2018-2025) reveals strong thematic continuity in AI applications, with recent emphasis on plastic waste characterization (2023-2024) and medical AI (2024-2025). The TÜBİTAK 1001 projects on football agility prediction and plastic aging demonstrate his capacity for securing competitive research funding. He has secured multiple TÜBİTAK 1001 grants for interdisciplinary research bridging computer engineering with environmental science and healthcare. His collaborative work spans medical diagnostics (fundoscopy devices, cerebral angiography), environmental monitoring (plastic degradation tracking), and industrial systems (electrical panel anomaly detection).

Tudor-Bogdan Airimitoaieمشاهده پروفایل

دانشیار

• Active noise and vibration control
• Adaptive regulation techniques
• Flat dynamic systems
• +۵ مورد دیگر

Tudor-Bogdan Airimitoaie is an Associate Professor in the Automatic Control group at IMS-lab, CNRS UMR 5218, University of Bordeaux. He leads research in adaptive control systems and flat dynamic systems within the Engineering and Digital Sciences Department. His work spans theoretical developments and practical applications in aerospace, automotive, and energy systems, with strong industry collaborations including Stellantis and Beyond the Sea. System's Engineering Diploma, University Politehnica of Bucharest, Romania (2008) PhD in Automatic Control, University of Grenoble and University Politehnica of Bucharest (2011-2012) Dr. Airimitoaie's research focuses on adaptive/learning algorithms and flat dynamic systems. His work addresses fundamental challenges in active noise and vibration control, with applications spanning wind energy systems, predictive maintenance, and convertible UAVs (combinations of VTOLs and conventional aircraft). He develops robust control algorithms that ensure safety and performance in complex dynamic environments, with particular emphasis on systems requiring fault tolerance and adaptive capabilities. His research bridges theoretical control principles with practical engineering implementations, often involving experimental validation on physical platforms. His recent publications demonstrate a strong trend toward integrating machine learning with traditional control theory, particularly in predictive maintenance applications. There's significant focus on accelerating adaptation algorithms through dynamic gain techniques and advancing fractional-order control methodologies. His work consistently addresses real-world challenges in aerospace (kite-based maritime transportation, convertible aircraft) and automotive systems (powertrain control), with increasing emphasis on sustainable energy solutions and safety-critical applications. Robert-Houdin award at the French seminar on Experimental Vibratory Analysis (2014) Best PhD award from the GdR MACS Best PhD in Automatic Control by the EEA Club (April 2013) Dr. Airimitoaie has supervised numerous PhD students working on convertible aircraft, tethered kite systems, and predictive maintenance applications. He has secured significant research funding as Principal Investigator for the ANR-funded MICA project (2016-2019) on fault detection for convertible UAVs, and as Co-investigator for the KiWin project (2023-) with Beyond the Sea on kite-based maritime transportation. His industry collaboration with Stellantis (2018-2022) on powertrain modeling and control demonstrates his ability to translate theoretical advances into practical automotive solutions. He also serves on editorial boards including IEEE Control Systems Letters starting in 2025. He is a key member of the CRONE and FFTG research teams within the Automatic Control group at IMS-lab. His laboratory work focuses on experimental validation of control algorithms using platforms including convertible aircraft demonstrators and tethered kite systems. The research environment supports both theoretical development and practical implementation of advanced control strategies, with strong connections to the broader control systems community through conference organization and technical committee memberships.

André Benine-Netoمشاهده پروفایل

دانشیار

• Automatic Control
• Vehicle Dynamics
• CRONE Control Systems
• +۳ مورد دیگر

André Benine-Neto is an Associate Professor at the University of Bordeaux, affiliated with the Laboratory of Integration from Material to System (IMS). He is a key member of the Automatic Control research group and contributes to the CRONE team specializing in advanced control methodologies for automotive applications. His primary research interests include: Automatic Control Systems Vehicle Dynamics and Control CRONE Control Methodology Suspension Systems Design Lateral Vehicle Guidance Advanced Driver Assistance Systems Professor Benine-Neto's research focuses on applying advanced control theory to automotive systems, particularly using the CRONE methodology. His work spans multiple aspects of vehicle dynamics control including lateral guidance for autonomous vehicles, suspension systems for improved ride comfort, and stability control systems. He has developed innovative approaches using piecewise affine control, multi-PID strategies, and the CRONE control framework, often collaborating with automotive industry partners like Stellantis (formerly PSA). His publication record shows consistent contributions over the past decade, with recent work (2023-2024) focusing on vertical road profile reconstruction, ultrasonic piezoelectric motor modeling, and advanced suspension control strategies. His research demonstrates strong integration of theoretical control methods with practical automotive applications, particularly in vehicle guidance, suspension systems, and fault-tolerant control. Professor Benine-Neto has also contributed to multiple patents related to vehicle suspension systems and lane guidance technologies, demonstrating the practical impact of his research. His work in the CRONE team represents a significant research effort in advanced control systems within the French academic landscape.

• Combustion Modeling
• Fluid Power
• Internal Combustion Engines
• +۳ مورد دیگر

Roberto Finesso is an Associate Professor at the Department of Energy (DENERG) at Politecnico di Torino, Italy. He is a member of the College of Mechanical, Aerospace and Automotive Engineering and contributes to the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His research focuses on combustion modeling and control in diesel engines, fluid power systems, and optimization of hybrid electric vehicle layouts and control strategies. Scientific Disciplinary Sector: IIND-06/A - Fluid Machinery ERC Sectors: PE8_5 - Fluid Mechanics, Hydraulic-, Turbo-, and Piston-Engines SDG Goals: 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), 12 (Responsible Consumption and Production) His work spans combustion diagnostics , NOx and PM emission control , hybrid powertrain optimization , and machine learning for engine calibration . He employs 3D-CFD (Fluent) and 0D (Amesim) techniques for hydraulic component modeling and develops real-time control algorithms for diesel engines. His research also includes model-based calibration , pressure-based combustion control , and predictive tools for emission analysis . Recent research trends focus on integrating physics-based , semi-empirical , and neural network models for combustion control. He has pioneered techniques for offline and real-time EGR optimization , MFB50 control , and IMEP management in diesel engines. His studies address both light-duty and heavy-duty applications, emphasizing CO2 minimization and cost efficiency in hybrid vehicles. Scientific Awards: SAE Excellence in Oral Presentation Award (2013) Teaching Contributions: Course Instructor for Fundamentals of Fluid Machinery and Fundamentals of Thermal and Hydraulic Machines and Fluid Power (Mechanical Engineering, 2019–2025) Course Collaborator for Automotive Fluid Power Systems (Automotive Engineering, 2019–2025) Research Groups: FPRL Research Group (DENERG)

• Hydraulic Fluid Power
• Fluid Mechanics
• Positive Displacement Pumps
• +۵ مورد دیگر

Massimo Rundo is an Associate Professor at the Polytechnic of Turin 's Department of Energy (DENERG) and coordinator of the Fluid Power Research Laboratory (FPRL) . With over 70 Scopus-indexed publications, his work focuses on modeling, simulation, and experimental validation of fluid power systems. Research Interests: Positive displacement pumps (gerotor, gear, vane, piston) Hydraulic valves (flow forces, pressure control) Mobile hydraulics (excavators, telehandlers) CFD and lumped parameter modeling Incomplete filling phenomena at high speeds Recent Publications analyze flow dynamics in pumps, vibration-based fault detection, and energy-efficient hydraulic architectures through 1D/3D simulations. He has coordinated industry-funded projects with companies like Casappa and Pierburg Pump Technology. Honors: MDPI Energies Editor's Choice (2018) SAE Excellence in Oral Presentation (2004) SAE Certificate of Appreciation (2000) Rundo teaches graduate courses in Mobile and Industrial Fluid Power and advises master's theses, while serving as Associate Editor for the International Journal of Fluid Power and committee member for major conferences including ASME/BATH Symposium and International Maha Fluid Power Conference.

• Acoustics
• Vibration Analysis
• Fluid Dynamics
• +۴ مورد دیگر

Wim De Roeck is an Associate Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology. He serves as contact person for the Mecha(tro)nic System Dynamics (LMSD) research group at Group T Leuven Campus and heads Subdivision 20 within the same campus. Additionally, he holds multiple program director roles for Elektromechanica programs across different KU Leuven campuses. His research focuses on acoustics, vibration analysis, and fluid dynamics with particular emphasis on aeroacoustics and flow-structure interactions. De Roeck's work bridges theoretical and experimental approaches to address noise and vibration challenges in mechanical systems. His research spans multiple application domains including automotive systems, aerospace components, and HVAC technologies, with a strong focus on micro-perforations, flow ducts, and Helmholtz resonators. Analysis of his recent publications reveals a consistent focus on advanced measurement techniques (particularly two-port and three-port characterization methods), impedance modeling of micro-scale components, and the development of both experimental and numerical approaches to understand flow-acoustic interactions. His work increasingly incorporates data-driven methodologies while maintaining strong foundations in fundamental acoustics and fluid mechanics principles. As an educator, De Roeck contributes to multiple courses including Strength of Materials for Machine Design, Finite Element Based Design, and Vehicle Dynamics, demonstrating his interdisciplinary expertise across mechanical engineering domains. He actively participates in institutional governance as a member of the Board and Council of the Faculty of Engineering Technology and serves as secretary for the POC Elektromechanica committee, reflecting his significant administrative contributions alongside his research and teaching responsibilities.